Dispersing steel fibers into plain concrete has been verified as an effective way to enhance the bearing capacity of structural components. To date, however, the effects of fibers on the basic mechanical properties of Steel Fiber Reinforced Concrete (SFRC) and their uncertainties are not well documented and there are no accurate models that can predict their mechanical properties. This greatly limits the application in real constructions. Considering such a situation, three aspects of works were conducted in this study to alleviate that difficulty, those are, building test database and checking the statistical distribution, reviewing and assessing existing models, and establishing highly-accurate models applied to estimate the compressive, tension and flexural properties of SFRC. To achieve the first two goals, a large database containing 1038 experimental records was compiled from existing publications and 145 predictive expressions were collected from the available literature. The last object was obtained by conducting Bayesian model updating analysis. It was finally found from the present study that: (a) Steel fibers can effectively enhance plain concrete's compressive, tensile and flexural strengths, but induce greater variability in those indexes. That variability is well described with a lognormal probability distribution. (b) Expressions proposed by Abbass and by Padmarajaiah generally give the most accurate predictions for the compressive strength and elastic modulus, while the formulas of Chinese code JG/T472–2015 and Padmarajaiah give the most accurate tensile and flexural strengths. (c) Because of combining advantages of the experience of the prior model and the accuracy of the test records, Bayesian updating method based analytical model usually possesses an obviously high precision.

Wang, Y., Jin, H., Demartino, C., Chen, W., Yu, Y. (2022). Mechanical properties of SFRC: Database construction and model prediction. CASE STUDIES IN CONSTRUCTION MATERIALS, 17(e01484), 1-17 [10.1016/j.cscm.2022.e01484].

Mechanical properties of SFRC: Database construction and model prediction

Demartino C.;
2022-01-01

Abstract

Dispersing steel fibers into plain concrete has been verified as an effective way to enhance the bearing capacity of structural components. To date, however, the effects of fibers on the basic mechanical properties of Steel Fiber Reinforced Concrete (SFRC) and their uncertainties are not well documented and there are no accurate models that can predict their mechanical properties. This greatly limits the application in real constructions. Considering such a situation, three aspects of works were conducted in this study to alleviate that difficulty, those are, building test database and checking the statistical distribution, reviewing and assessing existing models, and establishing highly-accurate models applied to estimate the compressive, tension and flexural properties of SFRC. To achieve the first two goals, a large database containing 1038 experimental records was compiled from existing publications and 145 predictive expressions were collected from the available literature. The last object was obtained by conducting Bayesian model updating analysis. It was finally found from the present study that: (a) Steel fibers can effectively enhance plain concrete's compressive, tensile and flexural strengths, but induce greater variability in those indexes. That variability is well described with a lognormal probability distribution. (b) Expressions proposed by Abbass and by Padmarajaiah generally give the most accurate predictions for the compressive strength and elastic modulus, while the formulas of Chinese code JG/T472–2015 and Padmarajaiah give the most accurate tensile and flexural strengths. (c) Because of combining advantages of the experience of the prior model and the accuracy of the test records, Bayesian updating method based analytical model usually possesses an obviously high precision.
2022
Wang, Y., Jin, H., Demartino, C., Chen, W., Yu, Y. (2022). Mechanical properties of SFRC: Database construction and model prediction. CASE STUDIES IN CONSTRUCTION MATERIALS, 17(e01484), 1-17 [10.1016/j.cscm.2022.e01484].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/438891
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